Natural Language Processing

The goal of this text is focus on a core subset of the natural language processing, unified by the concepts of learning and search. A remarkable number of problems in natural language processing can be solved by a compact set of methods: Search. Viterbi, CKY, minimum spanning tree, shift-reduce, integer linear programming, beam search. Learning. Maximum-likelihood estimation, logistic regression, perceptron, expectationmaximization, matrix factorization, backpropagation. This text explains how these methods work, and how they can be applied to a wide range of tasks: document classification, word sense disambiguation, part-of-speech tagging, named entity recognition, parsing, coreference resolution, relation extraction, discourse analysis, language modeling, and machine translation.